US11149618B2 - Catalyst deterioration detection device, catalyst deterioration detection system, data analysis device, control device of internal combustion engine, and method for providing sate information of used vehicle - Google Patents

Catalyst deterioration detection device, catalyst deterioration detection system, data analysis device, control device of internal combustion engine, and method for providing sate information of used vehicle Download PDF

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US11149618B2
US11149618B2 US16/778,518 US202016778518A US11149618B2 US 11149618 B2 US11149618 B2 US 11149618B2 US 202016778518 A US202016778518 A US 202016778518A US 11149618 B2 US11149618 B2 US 11149618B2
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catalyst
variable
amount
deterioration
data
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US20200263594A1 (en
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Harufumi Muto
Haruki Oguri
Akihiro Katayama
Yosuke Hashimoto
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Toyota Motor Corp
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Toyota Motor Corp
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N11/00—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N11/00—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity
    • F01N11/007—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity the diagnostic devices measuring oxygen or air concentration downstream of the exhaust apparatus
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
    • F01N3/08—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous
    • F01N3/10—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust
    • F01N3/18—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control
    • F01N3/20—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control specially adapted for catalytic conversion ; Methods of operation or control of catalytic converters
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N11/00—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity
    • F01N11/002—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity the diagnostic devices measuring or estimating temperature or pressure in, or downstream of the exhaust apparatus
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N13/00—Exhaust or silencing apparatus characterised by constructional features ; Exhaust or silencing apparatus, or parts thereof, having pertinent characteristics not provided for in, or of interest apart from, groups F01N1/00 - F01N5/00, F01N9/00, F01N11/00
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N9/00—Electrical control of exhaust gas treating apparatus
    • F01N9/007—Storing data relevant to operation of exhaust systems for later retrieval and analysis, e.g. to research exhaust system malfunctions
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2550/00—Monitoring or diagnosing the deterioration of exhaust systems
    • F01N2550/02—Catalytic activity of catalytic converters
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2560/00—Exhaust systems with means for detecting or measuring exhaust gas components or characteristics
    • F01N2560/02—Exhaust systems with means for detecting or measuring exhaust gas components or characteristics the means being an exhaust gas sensor
    • F01N2560/025—Exhaust systems with means for detecting or measuring exhaust gas components or characteristics the means being an exhaust gas sensor for measuring or detecting O2, e.g. lambda sensors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/04—Methods of control or diagnosing
    • F01N2900/0402—Methods of control or diagnosing using adaptive learning
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/04—Methods of control or diagnosing
    • F01N2900/0406—Methods of control or diagnosing using a model with a division of the catalyst or filter in several cells
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/04—Methods of control or diagnosing
    • F01N2900/0418—Methods of control or diagnosing using integration or an accumulated value within an elapsed period
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/06—Parameters used for exhaust control or diagnosing
    • F01N2900/0601—Parameters used for exhaust control or diagnosing being estimated
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/06—Parameters used for exhaust control or diagnosing
    • F01N2900/14—Parameters used for exhaust control or diagnosing said parameters being related to the exhaust gas
    • F01N2900/1402—Exhaust gas composition
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/06—Parameters used for exhaust control or diagnosing
    • F01N2900/16—Parameters used for exhaust control or diagnosing said parameters being related to the exhaust apparatus, e.g. particulate filter or catalyst
    • F01N2900/1602—Temperature of exhaust gas apparatus
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/06—Parameters used for exhaust control or diagnosing
    • F01N2900/16—Parameters used for exhaust control or diagnosing said parameters being related to the exhaust apparatus, e.g. particulate filter or catalyst
    • F01N2900/1624—Catalyst oxygen storage capacity
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the following description relates to a catalyst deterioration detection device for a catalyst provided in an exhaust passage of an internal combustion engine, a catalyst deterioration detection system, a data analysis device, a control device of an internal combustion engine, and a method for providing state information of a used vehicle.
  • Japanese Laid-Open Patent Publication No. 2012-117406 discloses an example of a device that intentionally controls the air-fuel ratio of a mixture that is burned in a combustion chamber to obtain a lean air-fuel ratio so that the oxygen storage amount of a catalyst is maximized.
  • the device intentionally controls the air-fuel ratio of the mixture to obtain a rich air-fuel ratio.
  • the device obtains a detection value of an air-fuel ratio sensor provided downstream of the catalyst.
  • the device detects that the oxygen storage amount of the catalyst becomes zero based on the acquired detection value of the air-fuel ratio sensor to calculate the maximum value of the oxygen storage amount of the catalyst. Since the maximum value of the oxygen storage amount decreases due to the deterioration of the catalyst over time, the maximum value indicates the deterioration level of the catalyst.
  • the air-fuel ratio of the mixture in order to detect the deterioration level of the catalyst, is deviated from a value appropriate to exhaust purification control. This may extend a period of time in which the air-fuel ratio of the mixture is deviated from the appropriate value or may increase the amount of deviation of the air-fuel ratio of the mixture from the appropriate value. As a result, deviation of the composition amount of a fluid flowing into the catalyst from a composition amount that is appropriate to the purification performance of the catalyst may accumulate, and the accumulated amount of deviation may increase.
  • a catalyst deterioration detection device configured to detect a deterioration of a catalyst provided in an exhaust passage of an internal combustion engine.
  • the catalyst deterioration detection device includes a storage device and processing circuitry.
  • the storage device stores map data.
  • the map data specifies a mapping that uses time series data of an excess amount variable in a first predetermined period and time series data of a downstream detection variable in a second predetermined period as inputs to output a deterioration level variable.
  • An amount of fuel that reacts with oxygen contained in a fluid flowing into the catalyst without excess or deficiency is an ideal fuel amount.
  • the excess amount variable is a variable that corresponds to an excess amount of an actual fuel flowing into the catalyst in relation to the ideal fuel amount.
  • the downstream detection variable is a variable that corresponds to a detection value of an air-fuel ratio sensor provided downstream of the catalyst.
  • the deterioration level variable is a variable related to a deterioration level of the catalyst.
  • the processing circuitry is configured to execute an acquisition process that acquires the time series data of the excess amount variable in the first predetermined period and the time series data of the downstream detection variable in the second predetermined period, a deterioration level variable calculation process that calculates the deterioration level variable of the catalyst based on an output of the mapping using the data acquired by the acquisition process as an input, and a dealing process that operates a predetermined hardware when the deterioration level of the catalyst is greater than or equal to a predetermined level based on a calculation result of the deterioration level variable calculation process in response to a situation in which the deterioration level of the catalyst is greater than or equal to the predetermined level.
  • the map data includes data that is learned through machine learning.
  • the deterioration level variable is calculated by the mapping that uses the time series data of the excess amount variable and the time series data of the downstream detection variable as inputs.
  • the excess amount variable refers to an excess amount of an actual fuel in relation to an amount of fuel that reacts with oxygen without excess or deficiency. When the actual fuel amount is deficient, the excess amount variable has a negative value. Since the time series data of the excess amount variable allows for obtainment of information regarding oxygen and fuel flowing into the catalyst, and the time series data of the downstream detection variable allows for obtainment of information regarding oxygen and unburned fuel flowing downstream of the catalyst, it is possible to obtain information on the maximum value of the oxygen storage amount of the catalyst. Furthermore, it is possible to obtain information regarding the deterioration level of the catalyst.
  • the air-fuel ratio of the mixture does not necessarily have to be deviated from a value that is appropriate for exhaust purification control. Even when the air-fuel ratio of the mixture is deviated from the appropriate value, the period of time in which the air-fuel ratio of the mixture is deviated from the proper value may be shortened, and the amount of deviation of the air-fuel ratio of the mixture from the appropriate value may be decreased. This reduces an accumulated amount of deviation of the composition amount of the fluid flowing into the catalyst from the composition amount that is appropriate to the purification performance of the catalyst.
  • the map data is learned through machine learning. Thus, the number of man-hours for associating the time series data of the excess amount variable and the time series data of the downstream detection variable with the deterioration level variable are reduced as compared to a case in which the adaptation is performed by humans.
  • the time series data in the second predetermined period includes values of the downstream detection variable that correspond to three or more different points in time.
  • an input to the mapping includes a temperature of the catalyst
  • the acquisition process includes a process that acquires the temperature of the catalyst
  • the deterioration level variable calculation process includes a process that calculates the deterioration level variable of the catalyst based on an output of the mapping that uses the temperature of the catalyst as an input.
  • the maximum value of the oxygen storage amount of the catalyst changes in accordance with the temperature of the catalyst.
  • the input to the mapping includes the temperature of the catalyst.
  • the deterioration level variable is calculated with high accuracy while determining whether the oxygen storage amount of the catalyst is reduced due to the deterioration or due to the temperature.
  • the excess amount variable includes a variable that corresponds to a detection value of an air-fuel ratio sensor provided upstream of the catalyst.
  • the excess amount variable includes a variable related to a detection value of the upstream air-fuel ratio sensor.
  • a period of time that is taken for the excess amount variable to affect a detection value of the downstream air-fuel ratio sensor is shortened as compared to a case in which only the amounts of fuel and air that are provided for combustion in a combustion chamber are used as the excess amount variables.
  • the configuration described above shortens a period of time from when the value of the excess amount variable is acquired to when the effect corresponding to the acquired value of the excess amount variable appears in the detection value of the downstream air-fuel ratio sensor. For this reason, the number of pieces of time series data is readily reduced as compared to a case in which only the amounts of fuel and air that are provided for combustion in the combustion chamber are used as the excess amount variables.
  • the map data is one of different kinds of map data
  • the storage device includes the different kinds of map data
  • the deterioration level variable calculation process includes a selection process that selects the map data from the different kinds of map data, the map data being used to calculate the deterioration level variable of the catalyst.
  • a single mapping is configured to be capable of outputting the deterioration level variable in various situations with high accuracy, the structure of the mapping becomes more complicated.
  • the configuration described above includes different kinds of map data.
  • an appropriate mapping is selected depending on each situation.
  • the structure of each of different kinds of mappings is simplified as compared to the structure of a single mapping in a case in which only the single mapping is provided.
  • the different kinds of map data include data for each of areas that are divided based on a flow rate of the fluid flowing into the catalyst, and the selection process includes a process that selects the map data used to calculate the deterioration level variable of the catalyst based on the flow rate.
  • the excess amount of the actual fuel in relation to the amount of fuel that reacts with oxygen contained in the fluid flowing into the catalyst without excess or deficiency may widely change depending on the level of the flow rate of the fluid flowing into the catalyst. Consequently, the oxygen storage amount of the catalyst may widely change per unit time. For this reason, the flow rate of the fluid flowing into the catalyst may significantly affect the time series data of the downstream detection variable. For this reason, if a single map data is used regardless of whether the flow rate of the fluid flowing into the catalyst is large or small, the behavior of the time series data of the downstream detection variable, which changes in accordance with the flow rate of the fluid, may need to be learned.
  • the different kinds of map data include data for each of areas that are divided based on a temperature of the catalyst, and the selection process includes a process that selects the map data used to calculate the deterioration level of the catalyst based on the temperature of the catalyst.
  • the behavior of the time series data of the downstream detection variable differs between the catalysts.
  • the behavior of the time series data of the downstream detection variable corresponding to the first catalyst differs from the behavior of the time series data of the downstream detection variable corresponding to the second catalyst.
  • the structure of the mapping becomes more complicated.
  • different pieces map data are used for each temperature of the catalyst.
  • the structure of the mapping is simplified.
  • the different kinds of map data include different pieces of data corresponding to whether a fuel cutoff process is being executed, and the selection process includes a process that selects the map data in accordance with whether or not the fuel cutoff process is being executed.
  • the behavior of the time series data of the downstream detection variable greatly differs depending on whether or not the fuel cutoff process is being executed. Regardless of the difference, if the deterioration level variable is calculated by a single map data, the structure of the mapping becomes more complicated. In this regard, in the configuration described above, different map data are used in accordance with whether or not the fuel cutoff process is being executed. Thus, the structure of the mapping is simplified.
  • the acquisition process includes a process that acquires a variable that is used as an input to the mapping when a predetermined condition is satisfied, and the predetermined condition includes a condition indicating that a flow rate of the fluid flowing into the catalyst is within a predetermined range.
  • the structure of the mapping becomes more complicated.
  • a sampling value corresponding to when the flow rate of the fluid flowing into the catalyst is within the predetermined range is used.
  • the mapping is specialized in a case in which the flow rate is within the predetermined range, and thus, the structure of the mapping is simplified.
  • the deterioration level variable of the catalyst is calculated based on the sampling value that is acquired when the flow rate of the fluid flowing into the catalyst is within the predetermined range.
  • the structure of the mapping is simplified as compared to a configuration in which the deterioration level variable of the catalyst is calculated regardless of the flow rate of the fluid flowing into the catalyst.
  • the acquisition process includes a process that acquires a variable that is used as an input to the mapping when a predetermined condition is satisfied, and the predetermined condition includes a condition indicating that a temperature of the catalyst is within a predetermined range.
  • the maximum value of the oxygen storage amount of a catalyst changes in accordance with the temperature of the catalyst, even when catalysts have the same catalyst deterioration level, if the catalysts have different temperatures, the behavior of the time series data of the downstream detection variable differs between the catalysts. For this reason, if the temperature-dependent differences in the behavior of the time series data of the downstream detection variable are configured to be distinguished, the structure of the mapping becomes more complicated. In this regard, in the configuration described above, a sampling value corresponding to when the temperature of the catalyst is within the predetermined range is used. Thus, the structure of the mapping is simplified.
  • the acquisition process includes a process that acquires a variable that is used as an input to the mapping in synchronization with a point in time at which a predetermined condition is satisfied, and the predetermined condition is a condition indicating that an amount of oxygen stored in the catalyst corresponds to a maximum value or a minimum value.
  • the behavior of the time series data of the downstream detection variable changes depending on the oxygen storage amount.
  • the behavior of the time series data of the downstream detection variable differs depending on the oxygen storage amount corresponding to when the oldest downstream detection variable was acquired among the time series data of the downstream detection variables.
  • the structure of the mapping becomes more complicated.
  • sampling is performed in synchronization with a point in time at which the oxygen storage amount reaches the maximum value or the minimum value. This allows for determination of a situation in which the time series data is acquired. As a result, the structure of the mapping is simplified.
  • time series data of the downstream detection variable is used as an input. This limits or eliminates the need for waiting until a large amount of oxygen or unburned fuel flows downstream of the catalyst in order to clearly determine a point in time at which the oxygen storage amount of the catalyst is switched from the maximum value to zero or from zero to the maximum value.
  • the dealing process includes a limiting process that limits an amount of unburned fuel flowing into the catalyst to a reduced amount.
  • the deterioration level When the deterioration level is high and a large amount of unburned fuel flows into the catalyst, it is difficult to sufficiently oxidize the unburned fuel with oxygen in the catalyst. This may result in an increase in the amount of unburned fuel flowing downstream of the catalyst.
  • the amount of unburned fuel flowing downstream of the catalyst when the deterioration level is higher than or equal to a predetermined level, the amount of unburned fuel flowing downstream of the catalyst is limited to a reduced amount. This limits an increase in the amount of unburned fuel flowing downstream of the catalyst.
  • a catalyst deterioration detection system includes the processing circuitry and the storage device according to any one of aspects 1 to 11.
  • the deterioration level variable calculation process includes an oxygen storage amount calculation process that uses at least a part of the map data to calculate a value corresponding to a maximum value of an oxygen storage amount of the catalyst.
  • the processing circuitry includes a first execution device and a second execution device.
  • the first execution device is installed in a vehicle and is configured to execute the acquisition process, a vehicle side transmission process that transmits data acquired by the acquisition process to an outside of the vehicle, a vehicle side reception process that receives a signal based on a calculation result of the oxygen storage amount calculation process, and the dealing process.
  • the second execution device is disposed outside the vehicle and is configured to execute an outside reception process that receives data transmitted by the vehicle side transmission process, the oxygen storage amount calculation process, and an outside transmission process that transmits a signal based on a calculation result of the oxygen storage amount calculation process to the vehicle.
  • the oxygen storage amount calculation process is executed outside the vehicle. This reduces the computational load of on-board devices.
  • a data analysis device includes the second execution device and the storage device according to aspect 13.
  • a control device of an internal combustion engine includes the first execution device according to aspect 13.
  • a method is for providing state information of a used vehicle on which an internal combustion engine is mounted.
  • the internal combustion engine is provided with a catalyst provided in an exhaust passage.
  • the method causes a computer to execute the acquisition process and the deterioration level variable calculation process according to any one of aspects 1 to 11, a storage process that stores a calculation result of the deterioration level variable calculation process together with a vehicle ID in a storage device, and an output process that outputs deterioration level information of the catalyst corresponding to the vehicle ID in response to an access from outside.
  • the deterioration level of the catalyst is useful information in determining how much it would cost, for example, to repair a used vehicle after the vehicle is purchased.
  • the calculation result of the deterioration level of the catalyst is stored together with the vehicle ID, and the information regarding the deterioration level is output in response to an access from outside. This provides useful information regarding the state of the used vehicle.
  • FIG. 1 is a view illustrating the configuration of a control device and a drive system of a vehicle according to a first embodiment.
  • FIG. 2 is a block diagram illustrating some of processes executed by the control device according to the first embodiment.
  • FIG. 3 is a flowchart illustrating the sequence of a process specified by a deterioration detection program according to the first embodiment.
  • FIG. 4 is a flowchart illustrating the sequence of a fail-safe process according to the first embodiment.
  • FIG. 5 is a diagram illustrating a system that generates map data according to the first embodiment.
  • FIG. 6 is a flowchart illustrating the sequence of a map data learning process according to the first embodiment.
  • FIG. 7 is a flowchart illustrating the sequence of a process specified by a deterioration detection program according to a second embodiment.
  • FIG. 8 is a flowchart illustrating the sequence of a process specified by a deterioration detection program according to a third embodiment.
  • FIG. 9 is a flowchart illustrating the sequence of a process specified by a deterioration detection program according to a fourth embodiment.
  • FIG. 10 is a flowchart illustrating the sequence of a process that selects map data according to a fifth embodiment.
  • FIG. 11 is a flowchart illustrating the sequence of a process specified by a deterioration detection program according to a sixth embodiment.
  • FIG. 12 is a flowchart illustrating the sequence of a process specified by a deterioration detection program according to a seventh embodiment.
  • FIG. 13 is a block diagram illustrating the configuration of a catalyst deterioration detection system and a dealer terminal according to an eighth embodiment.
  • FIG. 14 is a flowchart illustrating the sequence of a process executed by the catalyst deterioration detection system according to the eighth embodiment.
  • Exemplary embodiments may have different forms, and are not limited to the examples described. However, the examples described are thorough and complete, and convey the full scope of the disclosure to one of ordinary skill in the art.
  • a throttle valve 14 is provided in an intake passage 12 , and port injection valves 16 are provided downstream of the throttle valve 14 .
  • intake valves 18 open, air that is drawn from the intake passage 12 and fuel injected from the port injection valves 16 flow into combustion chambers 20 .
  • the internal combustion engine 10 is provided with in-cylinder injection valves 22 , which directly inject fuel into the combustion chambers 20 , and ignition devices 24 that generate spark discharges.
  • a mixture of air and fuel is provided for combustion in the combustion chambers 20 , and energy generated by the combustion is output as rotational energy of a crankshaft 26 .
  • exhaust valves 28 open, the mixture provided for the combustion is discharged to an exhaust passage 30 as exhaust.
  • the exhaust passage 30 is provided with an upstream catalyst 32 , which is a three-way catalyst and is capable of storing oxygen, and a downstream catalyst 34 which is a three-way catalyst and is capable of storing oxygen.
  • the exhaust passage 30 is connected to the intake passage 12 via an exhaust gas recirculation (EGR) passage 36 .
  • the EGR passage 36 is provided with an EGR valve 38 that regulates the flow path cross-sectional area of the EGR passage 36 .
  • the rotational power of the crankshaft 26 is transmitted to an intake camshaft 42 via an intake variable valve timing device 40 .
  • the rotational power of the crankshaft 26 is also transmitted to an exhaust camshaft 46 via an exhaust variable valve timing device 44 .
  • the intake variable valve timing device 40 changes a relative rotational phase difference between the intake camshaft 42 and the crankshaft 26 .
  • the exhaust variable valve timing device 44 changes a relative rotational phase difference between the exhaust camshaft 46 and the crankshaft 26 .
  • the crankshaft 26 of the internal combustion engine 10 is mechanically coupled to a carrier C of a planetary gear mechanism 50 , which configures a power split mechanism.
  • the planetary gear mechanism 50 includes a sun gear S that is mechanically coupled to a rotary shaft of a motor-generator (MG) 52 .
  • the planetary gear mechanism 50 includes a ring gear R that is mechanically coupled to a rotary shaft of a motor-generator 54 and drive wheels 56 .
  • Voltage is applied from a battery 62 to each terminal of the motor-generator 52 via an inverter 58 .
  • Voltage is applied from the battery 62 to each terminal of the motor-generator 54 via an inverter 60 .
  • the internal combustion engine 10 is controlled by a control device 70 .
  • the control device 70 operates operation units such as the throttle valve 14 , the port injection valve 16 , the in-cylinder injection valve 22 , the ignition device 24 , the EGR valve 38 , the intake variable valve timing device 40 , and the exhaust variable valve timing device 44 to control the control variables of the internal combustion engine 10 such as the torque and the exhaust composition ratio.
  • the control device 70 also controls the motor-generator 52 and operates the inverter 58 to control the torque and the rotational speed, which are control variables of the motor-generator 52 .
  • the control device 70 also controls the motor-generator 54 and operates the inverter 60 to control the torque and the rotational speed, which are control variables of the motor-generator 54 .
  • FIG. 1 shows operation signals MS 1 to MS 9 of the throttle valve 14 , the port injection valve 16 , the in-cylinder injection valve 22 , the ignition device 24 , the EGR valve 38 , the intake variable valve timing device 40 , the exhaust variable valve timing device 44 , and the inverters 58 and 60 respectively.
  • the control device 70 refers to an intake air amount Ga detected by an airflow meter 80 , an upstream detection value Afu, which is a detection value of an upstream air-fuel ratio sensor 82 provided upstream of the upstream catalyst 32 , or a downstream detection value Afd, which is a detection value of a downstream air-fuel ratio sensor 84 provided between the upstream catalyst 32 and the downstream catalyst 34 .
  • the control device 70 refers to an output signal Scr of a crank angle sensor 86 , an accelerator pedal operating amount ACCP, which is the amount of depression of an accelerator pedal detected by an accelerator pedal sensor 88 , or a vehicle speed SPD detected by a vehicle speed sensor 90 .
  • control device 70 refers to an atmospheric pressure Pa detected by an atmospheric pressure sensor 92 , an ambient temperature TO detected by an ambient temperature sensor 94 , a charging-discharging current I of the battery 62 detected by a current sensor 96 , and a terminal voltage V of the battery 62 detected by a voltage sensor 98 .
  • the control device 70 includes a CPU 72 , a ROM 74 , a storage device 76 , which is an electrically rewritable non-volatile memory, and peripheral circuitry 77 . These components are configured to communicate with each other through a local network 78 .
  • the peripheral circuitry 77 includes, for example, a circuit that generates a clock signal to specify an internal operation, a power supply circuit, and a reset circuit.
  • the control device 70 controls the above-described control variables through programs stored in the ROM 74 and executed by the CPU 72 .
  • FIG. 2 illustrates some of the processes implemented by the CPU 72 executing the programs stored in the ROM 74 .
  • a base injection amount calculation process M 10 calculates a base injection amount Qb, which is the base value of a fuel amount that sets the air-fuel ratio of the mixture in the combustion chamber 20 to a target air-fuel ratio, based on a charging efficiency ⁇ . More specifically, for example, in a case in which the charging efficiency ⁇ is expressed as a percentage, the base injection amount calculation process M 10 may calculate the base injection amount Qb by multiplying the charging efficiency ⁇ by a fuel amount QTH per 1% of the charging efficiency ⁇ , which sets the air-fuel ratio to the target air-fuel ratio.
  • the base injection amount Qb is an amount of fuel that is calculated so that the air-fuel ratio is controlled to the target air-fuel ratio based on the amount of air filling the combustion chamber 20 .
  • the target air-fuel ratio is a stoichiometric air-fuel ratio.
  • the charging efficiency ⁇ is a parameter that determines the amount air filling the combustion chamber 20 and is calculated by the CPU 72 based on a rotational speed NE and the intake air amount Ga.
  • the rotational speed NE is calculated by the CPU 72 based on the output signal Scr of the crank angle sensor 86 .
  • a feedback process M 12 calculates and outputs a feedback correction factor KAF by adding one to a correction ratio 6 of the base injection amount Qb, which is a feedback operating amount, that is, an operating amount that causes the upstream detection value Afu to be a target value Af* through feedback control. More specifically, the feedback process M 12 sets the correction ratio 5 to the sum of output values of a proportional control element and a derivative control element, which use the difference between the upstream detection value Afu and the target value Af* as an input, and an output value of an integral control element, which retains and outputs an integrated value of the value corresponding to the differences.
  • a request injection amount calculation process M 14 calculates a request injection amount Qd by multiplying the base injection amount Qb by the feedback correction factor KAF.
  • An injection valve operation process M 16 outputs the operation signal MS 2 to the port injection valve 16 and outputs the operation signal MS 3 to the in-cylinder injection valve 22 based on the request injection amount Qd to operate the port injection valve 16 and the in-cylinder injection valve 22 . More specifically, the injection valve operation process M 16 sets an injection division ratio Kp to the ratio of a fuel injection amount of the port injection valve 16 to the request injection amount Qd and operates the port injection valve 16 and the in-cylinder injection valve 22 in accordance with the injection division ratio Kp.
  • a sub-feedback process M 18 sets the target value Af* to be leaner than the stoichiometric point Afs by a specified amount ⁇ 1 .
  • the sub-feedback process M 18 sets the target value Af* to be richer than the stoichiometric point Afs by a specified amount ⁇ r.
  • a fuel cutoff process M 19 stops the injection of fuel from the port injection valve 16 and the in-cylinder injection valve 22 .
  • the injection valve operation process M 16 includes a process that performs an increase correction on the request injection amount Qd during a predetermined period from when the process is returned from the fuel cutoff process.
  • An ignition process M 20 outputs the operation signal MS 4 to the ignition device 24 to operate an ignition timing aig, which is an operating amount of the ignition device 24 .
  • the ignition process M 20 includes a process that normally sets a base ignition timing in accordance with the rotational speed NE and the charging efficiency ⁇ to set the ignition timing aig based on the base ignition timing.
  • the ignition process M 20 includes a process that sets the ignition timing aig based on an ignition timing that is retarded from the base ignition timing by a retard amount ⁇ aig during a warm-up process of the upstream catalyst 32 .
  • a catalyst temperature calculation process M 22 calculates a catalyst temperature Tcat, which is the temperature of the upstream catalyst 32 , based on the rotational speed NE, the charging efficiency ⁇ , the ignition timing aig, and the vehicle speed SPD. More specifically, the catalyst temperature calculation process M 22 is implemented in the following manner.
  • the ROM 74 stores in advance map data that uses the rotational speed NE and the charging efficiency ⁇ as input variables and the base temperature as an output variable
  • the CPU 72 obtains a base temperature through map calculation.
  • the CPU 72 obtains an ignition timing correction amount through map calculation.
  • the ROM 74 stores in advance map data that uses the vehicle speed SPD as an input variable and the vehicle speed correction amount, which is the correction amount of the base temperature based on the vehicle speed SPD, as an output variable
  • the CPU 72 obtains a vehicle speed correction amount through map calculation. Then, the CPU 72 corrects the base temperature using the ignition timing correction amount and the vehicle speed correction amount to calculate the catalyst temperature Tcat.
  • the map data is a data set of discrete values of an input variable and the value of an output variable corresponding to each value of the input variable. For example, when the value of an input variable matches any one of the values of the input variable in the map data, the map calculation may use the value of the corresponding output variable in the map data as the calculation result. When the value of the input variable does not match any one of the values of the input variable in the map data, the map calculation may use a value that is obtained by interpolating multiple values of the output variable in the map data as the calculation result.
  • An in-catalyst flow rate calculation process M 24 calculates an in-catalyst flow rate CF, which is the volumetric flow rate of a fluid flowing through the upstream catalyst 32 , based on the rotational speed NE and the charging efficiency ⁇ . More specifically, this process is implemented in the following manner. More specifically, the CPU 72 calculates the mass flow rate of the fluid flowing into the upstream catalyst 32 based on the charging efficiency ⁇ and the rotational speed NE. The CPU 72 estimates the pressure and temperature of the fluid flowing into the upstream catalyst 32 based on the rotational speed NE and the charging efficiency ⁇ , and converts the mass flow rate into a volumetric flow rate based on the estimated pressure and temperature.
  • the CPU 72 calculates the in-catalyst flow rate CF by converting the converted volumetric flow rate into a volumetric flow rate in the upstream catalyst 32 based on the ratio of the flow path cross-sectional area of the upstream catalyst 32 to the flow path cross-sectional area of the exhaust passage 30 upstream of the upstream catalyst 32 .
  • An output control process M 30 calculates an output Peg requested to the motor-generators 52 and 54 and an output Peg requested to the internal combustion engine 10 based on the accelerator pedal operating amount ACCP and the vehicle speed SPD. In particular, when a state of charge SOC of the battery 62 is greater than or equal to a predetermined value Sth, the output control process M 30 selects an EV mode in which the output Pmg of the internal combustion engine 10 is set to zero. When the state of charge SOC is less than the predetermined value Sth, the output control process M 30 selects an EHV mode in which the internal combustion engine 10 and the motor-generators 52 and 54 cooperate to ensure the requested output.
  • the state of charge SOC is calculated by the CPU 72 based on the terminal voltage V or the charging-discharging current I. More specifically, for example, when the absolute value of the charging-discharging current I is negligibly small, the CPU 72 assumes the terminal voltage V to be the open end voltage, and calculates the state of charge SOC based on a relationship between the open end voltage and the state of charge SOC. When the absolute value of the charging-discharging current I is large, the state of charge SOC is updated by the charging-discharging current I.
  • An MG control process M 32 outputs the operation signals MS 8 and MS 9 to the inverters 58 and 60 so that the sum of outputs of the motor-generators 52 and 54 becomes the output Pmg.
  • a throttle operation process M 34 outputs the operation signal MS 1 to the throttle valve 14 based on the output Peg so that the output of the internal combustion engine 10 becomes the output Peg.
  • control device 70 executes a process that calculates the deterioration level of the upstream catalyst 32 .
  • the process will be described in detail with reference to FIG. 3 and other drawings.
  • the process illustrated in FIG. 3 is implemented, for example, by the CPU 72 repeatedly executing a deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the number of each step is represented by the letter S followed by a numeral.
  • the CPU 72 acquires time series data for each of the upstream average value Afuave, a downstream average value Afdave, the in-catalyst flow rate CF, the rotational speed NE, the charging efficiency ⁇ , and the catalyst temperature Tcat in a predetermined period (S 10 ).
  • points in time of sampling are denoted by “1, 2, . . . , and sn” in order from the earliest one.
  • the time series data of the upstream average value Afuave are expressed as “Afuave (1) to Afuave (sn)”.
  • the term “sn” refers to the number of pieces of data included in the time series data of each variable. More specifically, the above-described predetermined period is set to a period in which “sn” pieces of data are sampled for each of the above-described variables. The above-described predetermined period is determined from a sampling time interval and the number of pieces of data “sn” and is not determined by a value such as the downstream detection value Afd.
  • the upstream average value Afuave is the average value of the upstream detection values Afu at an interval of the above-described time series data sampling. More specifically, the CPU 72 samples the upstream detection value Afu a number of times at an interval of the time series data sampling and calculates the average value of the upstream detection values Afu to set the average value to the upstream average value Afuave. In the same manner, the downstream average value Afdave is the average value of the downstream detection values Afd at an interval of the above-described time series data sampling.
  • the CPU 72 assigns the values that are acquired by step S 10 to input variables x (1) to x (6sn) of a mapping that outputs a deterioration level variable Rd, which is a variable indicating the deterioration level of the upstream catalyst 32 (S 12 ).
  • the CPU 72 assigns an upstream average value Afuave (m) to an input variable x (m), assigns a downstream average value Afdave (m) to an input variable x (sn+m), assigns an in-catalyst flow rate CF (m) to an input variable x (2sn+m), and assigns a rotational speed NE (m) to an input variable x (3sn+m).
  • the CPU 72 assigns a charging efficiency ⁇ (m) to an input variable x (4sn+m) and assigns a catalyst temperature Tcat (m) to an input variable x (5sn+m).
  • the CPU 72 calculates the deterioration level variable Rd, which is the output value of a mapping, by inputting the input variables x (1) to x (6sn) to a mapping that is specified by a map data 76 a stored in the storage device 76 illustrated in FIG. 1 (S 14 ).
  • a calculation of the output value of a mapping refers to a calculation of a variable, i.e. a calculation of the value of the variable.
  • the deterioration level variable Rd is quantified in the following manner.
  • the deterioration level variable Rd is zero.
  • the mapping is configured by a neural network in which the number of intermediate layers is “a,” activation functions h1 to h ⁇ of the intermediate layers are hyperbolic tangents, and activation function f of an output layer is a ReLU.
  • ReLU is a function that outputs the non-lesser one of an output and zero.
  • the node values of an m-th intermediate layer are generated by inputting the outputs of a linear mapping that is specified by a factor w(m) to an activation function hm.
  • n1, n2, . . . , and n ⁇ refers to the number of nodes in the first, a second, . . . , and an ⁇ -th intermediate layers.
  • w(1)j0 is one of bias parameter
  • an input variable x (0) is defined as one.
  • the CPU 72 determines whether or not the deterioration level variable Rd is greater than or equal to a specified value RdthH (S 16 ).
  • the CPU 72 executes a notification process that operates a warning lamp 99 illustrated in FIG. 1 to issue a notification (S 18 ).
  • the CPU 72 determines whether or not the deterioration level variable Rd is greater than or equal to a predetermined value RdthL (S 20 ).
  • the predetermined value RdthL is a value smaller than the specified value RdthH.
  • the CPU 72 sets a fail flag F to one (S 22 ).
  • step S 18 it is assumed that the fail flag F is already set to one.
  • the CPU 72 assigns zero to the fail flag F (S 24 ).
  • FIG. 4 illustrates the sequence of steps other than step S 18 in a process that deals with the deterioration of the upstream catalyst 32 .
  • the process illustrated in FIG. 4 is implemented, for example, by the CPU 72 repeatedly executing a fail-safe program 74 b stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the CPU 72 determines whether or not the fail flag F is one (S 30 ). When it is determined that the fail flag F is zero (S 30 : NO), the CPU 72 assigns a reference value Sth0 to the predetermined value Sth used in the output control process M 30 , assigns a reference amount ⁇ I0 to the specified amount ⁇ I and assigns a reference amount ⁇ r0 to the specified amount ⁇ r in the sub-feedback, and assigns a reference amount ⁇ aig0 to the retard amount ⁇ aig for a catalyst warm-up process (S 32 ).
  • the CPU 72 obtains a value by performing an increase correction on the reference value Sth0 using a predetermined amount ⁇ Sth and assigns the obtained value to the predetermined value Sth (S 34 ). Taking into consideration that the capacity of the upstream catalyst 32 for oxidizing unburned fuel is lowered due to the deterioration, this process maximizes the stop state of the internal combustion engine 10 so that the amount of unburned fuel flowing into the upstream catalyst 32 is limited to a reduced amount.
  • the CPU 72 obtains a value by performing a decrease correction on the reference amount ⁇ I0 using a correction amount ⁇ I and assigns the obtained value to the specified amount ⁇ I, and obtains a value by performing a decrease correction on the reference amount ⁇ r0 using a correction amount ⁇ r and assigns the obtained value to the specified amount ⁇ r (S 36 ).
  • this process limits the amount of unburned fuel flowing into the upstream catalyst 32 to a reduced amount. More specifically, in a case in which the fail flag F is one, the specified amount ⁇ r is set to be a smaller value than in a case in which the fail flag F is zero.
  • the CPU 72 obtains a value by performing an increase correction on the reference amount ⁇ aig0 using a correction amount ⁇ F and assigns the obtained value into the retard amount ⁇ aig (S 38 ).
  • the upstream catalyst 32 deteriorates, the exhaust purification performance of the upstream catalyst 32 is lower than when the upstream catalyst 32 does not deteriorate.
  • the above-described process reduces the ratio of conversion of the combustion energy of the mixture into torque to increase the temperature of exhaust discharged to the exhaust passage 30 so that the upstream catalyst 32 is wormed up at an early time.
  • FIG. 5 illustrates a system which generates the map data 76 a.
  • a dynamometer 100 is mechanically coupled to the crankshaft 26 of the internal combustion engine 10 .
  • a sensor group 102 detects various state variables of the internal combustion engine 10 .
  • the detection results are input to an adaptation device 104 , which is a computer that generates the map data 76 a .
  • the sensor group 102 includes sensors that detect values for generating inputs to a mapping such as the upstream air-fuel ratio sensor 82 , the downstream air-fuel ratio sensor 84 , and the crank angle sensor 86 .
  • FIG. 6 illustrates the sequence of generating map data.
  • the process illustrated in FIG. 6 is executed by the adaptation device 104 .
  • the process illustrated in FIG. 6 may be implemented, for example, by providing the adaptation device 104 with a CPU and a ROM and causing the CPU to execute programs stored in the ROM.
  • the adaptation device 104 acquires the same data as that acquired in step S 10 , as training data (S 40 ).
  • training data S 40
  • the process described above is executed so that the installed upstream catalyst 32 has a deterioration level variable Rdt that is used as teacher data.
  • the adaptation device 104 assigns the training data other than the teacher data to the input variables x (1) to x (6sn) (S 42 ). Then, in accordance with the procedure of step S 14 , the adaptation device 104 calculates the deterioration level variable Rd by inputting the input variables x (1) to x (6sn) obtained by step S 42 into a mapping (S 44 ). Then, the CPU 72 determines whether or not the number of samples of the deterioration level variables Rd calculated by step S 44 is greater than or equal to a predetermined number (S 46 ).
  • the deterioration level variable Rd needs to be calculated two times or more for each of the above-described upstream catalysts 32 . Furthermore, the deterioration level variable Rd needs to be calculated at various operating points that are specified by the rotational speed NE and the charging efficiency ⁇ in accordance with changes in the operation mode of the internal combustion engine 10 .
  • the adaptation device 104 When it is determined that the number of samples is not greater than or equal to the predetermined number (S 46 : NO), the adaptation device 104 returns to step S 40 . When it is determined that the number of samples is greater than or equal to the predetermined number (S 46 : YES), the CPU 72 updates factors w(1)ji, w(2)kj, . . . , and w( ⁇ )1p to minimize the sum of the squares of the differences between the deterioration level variable Rdt as the teacher data and the deterioration level variable Rd calculated by step S 44 (S 48 ). Then, the adaptation device 104 stores the factors w(1)ji, w(2)kj, . . . , and w( ⁇ )1p as the map data 76 a that is learned (S 50 ).
  • the map data 76 a is learned to specify a mapping that uses the time series data of each of the upstream average value Afuave, the downstream average value Afdave, the in-catalyst flow rate CF, the rotational speed NE, the charging efficiency ⁇ , and the catalyst temperature Tcat, as inputs to output the deterioration level variable Rd.
  • the rotational speed NE and the charging efficiency r which specify the operating points of the internal combustion engine 10 , may be regarded as the flow rate variables indicating the flow rate of the fluid which flows into the upstream catalyst 32 .
  • the upstream average value Afuave is a variable indicating the ratio of the amount of actual fuel to the amount of fuel that reacts with oxygen contained in the fluid flowing into the upstream catalyst 32 without excess or deficiency.
  • the amount of fuel that reacts with oxygen contained in the fluid flowing into the upstream catalyst 32 without excess or deficiency is referred to as the ideal fuel amount. Therefore, the upstream average value Afuave, the rotational speed NE, and the charging efficiency ⁇ collectively configure an excess amount variable, that is, a variable corresponding to an excess amount of the actual fuel in relation to the amount of fuel that reacts with oxygen contained in the fluid flowing into the upstream catalyst 32 without excess or deficiency.
  • the excess amount may have a negative value. In other words, the excess amount may have a value obtained by multiplying “ ⁇ 1” by the deficient amount of the actual fuel in relation to the amount of fuel that reacts with oxygen contained in the fluid flowing into the upstream catalyst 32 without excess or deficiency.
  • the behavior of the downstream average value Afdave changes in accordance with the maximum value of the oxygen storage amount of the upstream catalyst 32 in addition to the above-described excess amount in the fluid flowing into the upstream catalyst 32 .
  • the maximum value of the oxygen storage amount changes in accordance with not only the deterioration level of the upstream catalyst 32 but also the temperature of the upstream catalyst 32 . Therefore, it may be assumed that the deterioration level variable Rd of the upstream catalyst 32 is calculated by inputting time series data indicating the behavior of the downstream average value Afdave together with the time series data of the excess amount variable and the time series data of the catalyst temperature Tcat.
  • the present embodiment does not use a mapping that is learned through machine learning by inputting a large number of various random variables of the internal combustion engine 10 to calculate the deterioration level variable Rd.
  • the variables that are input to the mapping are carefully selected based on their relevance to the control of the internal combustion engine 10 .
  • the number of the intermediate layers of the neural network and the number of data sn of the time series data are reduced as compared to a case in which the variables that are input to the mapping are not carefully selected based on their relevance to the control of the internal combustion engine 10 .
  • the structure of the mapping that calculates the deterioration level variable Rd may be simplified.
  • the behavior of the downstream average value Afdave for the excess amount variable and the catalyst temperature Tcat is recognized from the time series data of these variables.
  • the deterioration level variable Rd is calculated without directly detecting that the oxygen storage amount of the upstream catalyst 32 has reached zero or the maximum value. Since the deterioration level variable Rd is calculated without changing the target value Af* for the detection of the deterioration level, the accumulated amount of deviation of the composition of the fluid flowing into the upstream catalyst 32 from the composition that is appropriate for the purification performance of the upstream catalyst 32 is reduced.
  • the input to the mapping includes the upstream average value Afuave.
  • the upstream detection value Afu for each time interval of the time series data is used, further accurate information regarding oxygen and unburned fuel flowing into the upstream catalyst 32 is obtained without increasing the number of data pieces of the time series data.
  • the deterioration level variable Rd is calculated with higher accuracy.
  • the input to the mapping includes the downstream average value Afdave.
  • the downstream detection value Afd for each time interval of the time series data is used, further accurate information regarding oxygen and unburned fuel flowing out from the upstream catalyst 32 is obtained without increasing the number of data pieces of the time series data.
  • the deterioration level variable Rd is calculated with higher accuracy.
  • the input to the mapping includes the rotational speed NE and the charging efficiency ⁇ , which are used as operating point variables specifying the operating points of the internal combustion engine 10 .
  • the operating amounts of the operation units such as the ignition device 24 , the EGR valve 38 , and the intake variable valve timing device 40 of the internal combustion engine 10 tend to be determined based on the operating point of the internal combustion engine 10 .
  • the operating point variable is a variable that not only configures the flow rate variable, together with the upstream average value Afuave, but also includes information related to the operating amount of each operation unit. Therefore, the deterioration level variable Rd is calculated based on the information related to the operating amount of each operation unit by inputting the operating point variable to the mapping. Ultimately, the deterioration level variable Rd is calculated with higher accuracy.
  • the input to the mapping includes the in-catalyst flow rate CF.
  • the in-catalyst flow rate CF is a variable that affects the rate of reaction between unburned fuel and oxygen in the upstream catalyst 32 . For this reason, when the in-catalyst flow rate CF is input to the mapping, the deterioration level variable Rd is calculated with higher accuracy.
  • the in-catalyst flow rate CF is calculated from the operating point variables of the internal combustion engine 10 .
  • the input to the mapping includes the operating point variables
  • the effect of the in-catalyst flow rate CF on the rate of reaction between unburned fuel and oxygen in the upstream catalyst 32 is reflected during calculation of the deterioration level variable Rd without using the in-catalyst flow rate CF.
  • the number of intermediate layers in the neural network and the number sn of pieces of data in the time series data tend to increase.
  • the structure of the mapping may be simplified.
  • the output of the neural network is a maximum value Cmax of the current oxygen storage amount of the upstream catalyst 32 .
  • FIG. 7 illustrates the sequence of a process executed by the control device 70 in the present embodiment.
  • the process illustrated in FIG. 7 is implemented, for example, by the CPU 72 repeatedly executing the deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the same step numbers are given to the steps corresponding to the steps illustrated in FIG. 3 .
  • step S 10 when step S 10 is completed, the CPU 72 inputs the time series data other than the catalyst temperature Tcat to the input variables x (1) to x (5sn) of the mapping (S 12 a ), instead of executing step S 12 .
  • the same variables as used in step S 12 are assigned to the input variables x (1) to x (5sn).
  • the CPU 72 calculates the maximum value Cmax through a neural network that uses the input variables x (1) to x (5sn) as inputs and uses the maximum value Cmax as an output (S 14 a ).
  • the CPU 72 uses map data that uses a catalyst temperature average value Tcatave, which is the average value of the catalyst temperatures Tcat acquired in S 10 , and the maximum value Cmax as input variables and uses the deterioration level variable Rd as an output variable to obtain the deterioration level variable Rd through map calculation (S 52 ).
  • the CPU 72 proceeds to step S 16 .
  • the factors w(1)ji, w(2)kj, . . . , and w( ⁇ )1p of the neural network may be learned using the following teacher data. More specifically, the relationship between the temperature and the maximum value Cmax of each of the above-described upstream catalysts 32 may be measured in advance. In a step corresponding to the step illustrated in FIG. 6 , the current maximum value Cmax of the corresponding one of the target upstream catalysts 32 may be obtained and used as teacher data.
  • average values of the time series data of each of the in-catalyst flow rate CF, the rotational speed NE, the charging efficiency ⁇ , and the catalyst temperature Tcat are calculated and used as inputs to the mapping.
  • the number sn of pieces of data in the time series data of each variable acquired by step S 10 is a multiple of five.
  • FIG. 8 illustrates the sequence of a process executed by the control device 70 in the present embodiment.
  • the process illustrated in FIG. 8 is implemented, for example, by the CPU 72 repeatedly executing the deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the same step numbers are given to the steps corresponding to the steps illustrated in FIG. 3 .
  • step S 10 when step S 10 is completed, the CPU 72 calculates an average value of each set of “sn/5” elements in order from the earliest element in each of the in-catalyst flow rates CF, the rotational speeds NE, the charging efficiencys ⁇ , and the catalyst temperatures Tcat (S 60 ). More specifically, for example, an in-catalyst flow rate average value CFave (1) is calculated as the average value of in-catalyst flow rates CF (1), CF (2), . . . , and CF (sn/5).
  • An in-catalyst flow rate average value CFave (2) is calculated as the average value of in-catalyst flow rates CF ((sn/5)+1), CF ((sn/5)+2), . . . , and CF (2n/5).
  • time series data including five sets of in-catalyst flow rate average values CFave, time series data including five sets of rotational speed average values NEave, time series data including five sets of charging efficiency average values ⁇ ave, and time series data including five sets of catalyst temperature average values Tcatave are produced.
  • the CPU 72 assigns an in-catalyst flow rate average value CFave (m) to the input variable x (2sn+m), and assigns a rotational speed average value NEave (m) to an input variable x (2sn+5+m).
  • the CPU 72 assigns a charging efficiency average value ⁇ ave (m) to an input variable x (2sn+10+m), and assigns a catalyst temperature average value Tcatave (m) to an input variable x (2sn+15+m).
  • the CPU 72 calculates the deterioration level variable Rd through a neural network that uses the input variables x (1) to x (2sn+20) generated in S 12 b as inputs and uses the deterioration level variable Rd as an output (S 14 b ).
  • step S 14 b the CPU 72 proceeds to step S 16 .
  • the in-catalyst flow rate average value CFave, the rotational speed average value NEave, the charging efficiency average value ⁇ ave, and the catalyst temperature average value Tcatave are used as the inputs to the mapping. This reduces the dimensions of the input to the mapping.
  • the excess amount variable is collectively configured by the time series data of the upstream average value Afuave, the time series data of the rotational speed average value NEave, and the time series data of the charging efficiency average value ⁇ ave.
  • the rotational speed average value NEave and the charging efficiency average value ⁇ ave represent the operating points of the internal combustion engine 10 in a period in which “sn/5” sets of the upstream average values Afuave are sampled.
  • the storage device 76 stores different map data for each of the operating point of the internal combustion engine 10 and the catalyst temperature Tcat.
  • FIG. 9 illustrates the sequence of a process executed by the control device 70 in the present embodiment.
  • the process illustrated in FIG. 9 is implemented, for example, by the CPU 72 repeatedly executing the deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the same step numbers are given to the steps corresponding to the steps illustrated in FIG. 3 .
  • the CPU 72 selects map data for calculating the deterioration level variable Rd in accordance with the rotational speed NE and the charging efficiency n, which specify the operating point of the internal combustion engine 10 , and the catalyst temperature Tcat (S 62 ).
  • This process can be implemented, for example, when the ROM 74 stores in advance map data that uses the rotational speed NE, the charging efficiency ⁇ , and the catalyst temperature Tcat as input variables and uses the variable specifying the map data as an output variable, by the CPU 72 obtaining a variable that specifies the map data through map calculation.
  • the CPU 72 acquires time series data of each of the upstream average value Afuave and the downstream average value Afdave (S 10 c ).
  • the CPU 72 calculates the deterioration level variable Rd through a neural network that uses the input variables x (1) to x (2sn) generated in step S 12 c as inputs and uses the deterioration level variable Rd as an output (S 14 c ).
  • step S 14 c When step S 14 c is completed, the CPU 72 proceeds to step S 16 .
  • the deterioration level variable Rd is calculated using different map data in accordance with the operating point of the internal combustion engine 10 and the catalyst temperature Tcat.
  • the CPU 72 calculates the deterioration level variable Rd using different map data. Therefore, when a mapping specified by a single piece of map data is used, the flow rate of the fluid flowing into the upstream catalyst 32 does not widely change. In this case, the excess amount of an actual fuel in relation to the amount of fuel that reacts with oxygen contained in the fluid flowing into the upstream catalyst 32 without excess or deficiency may be recognized simply from the upstream average value Afuave.
  • the above-described excess amount variable may be configured by only the upstream average value Afuave.
  • the catalyst temperature Tcat does not greatly change.
  • the deterioration level of the upstream catalyst 32 is larger than when the maximum value Cmax is large. More specifically, the deterioration level is quantified in accordance with the maximum value Cmax at the current temperature.
  • the dimensions of the variables that are input to the mapping are reduced.
  • the number n ⁇ of the intermediate layers is reduced. Therefore, in the present embodiment, the structure of the mapping is simplified.
  • the storage device 76 stores three types of map data as the map data 76 a.
  • FIG. 10 illustrates the sequence of a process that selects any one of the three types of map data to calculate the deterioration level variable Rd.
  • the process illustrated in FIG. 10 is implemented, for example, by the CPU 72 repeatedly executing the deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the CPU 72 determines whether or not the fuel cutoff process is being executed (S 70 ). Then, when it is determined that the fuel cutoff process is being executed (S 70 : YES), the CPU 72 selects first map data (S 74 ).
  • the first map data is map data that is dedicated to when the fuel cutoff process is executed, and is learned by using data in the fuel cutoff process as training data.
  • the CPU 72 determines whether or not it is within the above-described predetermined period after the fuel cutoff process (S 72 ). Then, when it is determined that it is within the predetermined period (S 72 : YES), the CPU 72 selects a second map data (S 76 ).
  • the second map data is learned by using time series data that is sampled in the predetermined period after the fuel cutoff process as training data. More specifically, the second map data is learned by using time series data that is sampled during a period in which an increase correction is performed on the request injection amount Qd as training data.
  • the CPU 72 selects a third map data (S 78 ).
  • the third map data is learned by using time series data that is sampled in a period that excludes the fuel cutoff process and the predetermined period after the fuel cutoff process as training data.
  • the deterioration level variable Rd is calculated by using different map data for when the fuel cutoff process is being executed, the predetermined period after the fuel cutoff process, and the period outside of these specifically described periods.
  • the fuel cutoff process a large amount of oxygen flows into the upstream catalyst 32 , and the air-fuel ratio of the mixture, which is subject to combustion, tends to be disturbed in the predetermined period after the fuel cutoff process. For this reason, as compared to a case in which single map data is used for these different situations, the structure of the mapping may be simplified.
  • the present embodiment aims to simplify the structure of the mapping by limiting conditions that allow the sampling of variables that are used to calculate the deterioration level variable Rd.
  • FIG. 11 illustrates the sequence of a process executed by the control device 70 in the present embodiment.
  • the process illustrated in FIG. 11 is implemented, for example, by the CPU 72 repeatedly executing the deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the same step numbers are given to the steps corresponding to the steps illustrated in FIG. 3 .
  • the CPU 72 determines whether or not the logical product of the following conditions (A) to (C) is true (S 80 ).
  • Condition (A) is a condition indicating that the rotational speed NE is greater than or equal to a first speed NEL and less than or equal to a second speed NEH.
  • Condition (B) is a condition indicating that the charging efficiency r1 is greater than or equal to a first charging efficiency ⁇ L and less than or equal to a second charging efficiency ⁇ H.
  • Condition (C) is a condition indicating that the catalyst temperature Tcat is greater than or equal to a first temperature TcatL and less than or equal to a second temperature TcatH.
  • the deterioration level variable Rd is calculated only in a case in which the operating point of the internal combustion engine 10 is within in the predetermined range and the catalyst temperature Tcat is within the predetermined range.
  • the limitation imposed on the range of the operating point allows calculation of the deterioration level variable Rd only in a case in which the flow rate of the fluid flowing into the upstream catalyst 32 does not widely differ from a reference value. In this case, an excess amount of an actual fuel in relation to the amount of fuel that reacts with oxygen contained in the fluid flowing into the upstream catalyst 32 without excess or deficiency may be recognized simply from the upstream average value Afuave.
  • the above-described excess amount variable may be configured only by the upstream average value Afuave.
  • the limitation imposed on the range of the catalyst temperature Tcat allows calculation of the deterioration level variable Rd only in a case in which the catalyst temperature Tcat does not widely differ from a reference temperature. In this case, when the maximum value Cmax of the oxygen storage amount is small, the deterioration level of the upstream catalyst 32 is larger than when the maximum value Cmax is large. More specifically, the deterioration level is quantified in accordance with the maximum value Cmax at the current temperature.
  • the dimensions of the variables that are input to the mapping are reduced.
  • the number n ⁇ of the intermediate layers is reduced. Therefore, in the present embodiment, the structure of the mapping may be simplified.
  • the present embodiment aims to simplify the structure of the mapping by limiting conditions that allow the sampling of variables that are used to calculate the deterioration level variable Rd.
  • FIG. 12 illustrates the sequence of a process executed by the control device 70 in the present embodiment.
  • the process illustrated in FIG. 12 is implemented, for example, by the CPU 72 repeatedly executing the deterioration detection program 74 a stored in the ROM 74 illustrated in FIG. 1 at predetermined time intervals.
  • the same step numbers are assigned to the steps corresponding to the steps illustrated in FIG. 3 .
  • the CPU 72 determines whether or not the logical sum of a condition (D) that indicates a return from the fuel cutoff process and a condition (E) indicating that the oxygen storage amount is zero is true (S 82 ). In this step, it is determined whether or not the oxygen storage amount of the upstream catalyst 32 is zero or the maximum value Cmax. Whether the oxygen storage amount is zero may be detected, for example, when the feedback control of the air-fuel ratio is greatly deviated and the downstream detection value Afd is set to be rich shortly after the upstream detection value Afu is set to be rich.
  • step S 82 When it is determined that the logical sum is true (S 82 : YES), the CPU 72 executes steps S 10 to S 24 .
  • step S 14 When the step S 14 is executed once, the CPU 72 does not calculate the deterioration level variable Rd until it is determined again in step S 82 that the logical sum is true.
  • the initial value of the time series data that is input to the mapping can be fixed when the oxygen storage amount of the upstream catalyst 32 corresponds to the maximum value Cmax or zero.
  • the mapping that outputs the deterioration level variable Rd may be a mapping that uses time series data corresponding to when the oxygen storage amount of the upstream catalyst 32 corresponds to the maximum value Cmax or zero as an input and outputs the deterioration level variable Rd.
  • the structure of the mapping may be simplified as compared to a mapping that outputs the deterioration level variable Rd in any situation.
  • the process that calculates the deterioration level variable Rd is performed outside the vehicle.
  • FIG. 13 illustrates a catalyst deterioration detection system according to the present embodiment.
  • the same reference characters are given to the members corresponding to the members illustrated in FIG. 1 .
  • the control device 70 installed in the vehicle VC illustrated in FIG. 13 includes a communication device 79 .
  • the communication device 79 is a device for communicating with a center 120 via a network 110 outside the vehicle VC.
  • the center 120 analyzes data received from a plurality of vehicles VC.
  • the center 120 includes a CPU 122 , a ROM 124 , a storage device 126 , peripheral circuitry 127 , and a communication device 129 , and these devices are configured to communicate with each other through a local network 128 .
  • the ROM 124 stores a deterioration detection main program 124 a .
  • the storage device 126 stores a map data 126 a.
  • the center 120 is configured to communicate with a terminal 130 of a used vehicle dealer through the network 110 .
  • FIG. 14 shows the sequence of a process executed by the system illustrated in FIG. 13 .
  • the process illustrated in (a) in FIG. 14 is implemented by the CPU 72 executing a deterioration detection sub-program 74 c stored in the ROM 74 illustrated in FIG. 13 .
  • the process illustrated in (b) in FIG. 14 is implemented by the CPU 122 executing the deterioration detection main program 124 a and a state information provision program 124 b that are stored in the ROM 124 .
  • the same step numbers are given to the steps corresponding to the steps illustrated in FIG. 2 .
  • the processes illustrated in FIG. 14 will be described along the time series of a deterioration detection process.
  • the CPU 72 installed in the vehicle VC acquires variables as the input to the mapping in addition to the time series data acquired in step S 10 (S 90 ). More specifically, the CPU 72 acquires time series data of an increase amount average value Qiave, which is the average value of increase amounts Qi of the request injection amount Qd from the base injection amount Qb.
  • the increase amount Qi may have a negative value.
  • the increase amount Qi indicates an excess or deficiency amount of the actual fuel in relation to the amount of fuel that sets the air-fuel ratio of the mixture to the stoichiometric air-fuel ratio.
  • the increase amount Qi configures the excess amount variable.
  • the CPU 72 acquires the following variables as variables that are related to the operating amounts of the operation units of the internal combustion engine 10 .
  • the variables change the combustion of the mixture in the combustion chamber 20 to change the compositions of the fluid flowing into the upstream catalyst 32 .
  • the CPU 72 acquires time series data of an ignition timing average value aigave, which is an average value of the ignition timings aig that are set by the ignition device 24 .
  • the CPU 72 acquires time series data of an EGR rate average value Regrave, which is an average value of EGR rates Regr, that is, the ratio of the flow rate of the fluid to the sum of the flow rate of air taken into the intake passage 12 and the flow rate of the same fluid flowing from the EGR passage 36 into the intake passage 12 .
  • the CPU 72 acquires time series data of an overlap average value ROave, which is an average value of overlaps RO, that is, a period in which the valve opening period of the intake valve 18 overlaps the valve opening period of the exhaust valve 28 .
  • the CPU 72 acquires time series data of an injection division ratio average value Kpave, which is an average value of the injection division ratios Kp.
  • the CPU 72 acquires an alcohol concentration Dal in the fuel. This variable is acquired taking into consideration that the stoichiometric air-fuel ratio of the fuel changes in accordance with alcohol concentration.
  • the alcohol concentration Dal may be estimated from, for example, the correction ratio 5 of the feedback process M 12 described above.
  • the CPU 72 acquires the atmospheric pressure Pa and the ambient temperature TO as variables that are related to the environments and change the combustion of the mixture in the combustion chamber 20 to change the compositions of the fluid flowing into the upstream catalyst 32 .
  • the CPU 72 acquires a sulfur deposition amount Qs of the upstream catalyst 32 , which is one of the state variables of the upstream catalyst 32 that is related to changing over time. This variable is acquired taking into consideration that the purification capacity of the upstream catalyst 32 changes in accordance with the sulfur deposition amount Qs.
  • the CPU 72 calculates the sulfur deposition amount Qs through a process that integrates a value obtained by multiplying the request injection amount Qd by a predetermined factor.
  • the CPU 72 acquires the maximum value Cmax at the reference temperature, a length Lud from an upstream side to a downstream side, and a support amount Qpm of a noble metal as specification variables, that is, the state variables of the upstream catalyst 32 indicating the specifications. This is a setting for calculating the deterioration level of the upstream catalyst 32 having various specifications using single map data.
  • step S 90 the CPU 72 operates the communication device 79 to transmit the data acquired in step S 90 to the center 120 , together with a vehicle ID, that is, identification information of the vehicle VC (S 92 ).
  • the CPU 122 of the center 120 receives the transmitted data (S 96 ), and assigns the data acquired by step S 90 to the input variable x of the mapping (S 12 d ).
  • the CPU 122 assigns an increase amount average value Qiave (m) to an input variable x (6sn+m), assigns an ignition timing average value aigave (m) to an input variable x (7sn+m), and assigns an EGR rate average value Regrave (m) to an input variable x (8sn+m).
  • the CPU 122 assigns an overlap average value ROave (m) to an input variable x (9sn+m), and assigns an injection division ratio average value Kpave (m) to an input variable x (10sn+m).
  • the CPU 122 assigns the alcohol concentration Dal to an input variable x (11 sn+1), assigns the atmospheric pressure Pa to an input variable x (11sn+2), assigns the ambient temperature TO to an input variable x (111sn+3), and assigns the sulfur deposition amount Qs to an input variable x (11 sn+4).
  • the CPU 122 assigns the maximum value Cmax to an input variable x (11sn+5), assigns the length Lud to an input variable x (11 sn+6), and assigns the support amount Qpm to an input variable x (11 sn+7).
  • the CPU 122 calculates the deterioration level variable Rd by inputting the input variables x (1) to x (11sn+7) generated by S 12 d to a mapping that is specified by the map data 126 a (S 14 d ).
  • the CPU 122 operates the communication device 129 to transmit a signal related to the deterioration level variable Rd to the vehicle VC from which the data is received in step S 96 (S 98 ).
  • the CPU 72 receives the deterioration level variable Rd (S 94 ), and executes steps S 16 to S 24 .
  • the CPU 122 updates the deterioration level variable Rd related to the vehicle specified by the vehicle ID (S 100 ) in state information data 126 b that is stored in the storage device 126 illustrated in FIG. 13 .
  • the CPU 122 determines whether or not the terminal 130 of the used vehicle dealer sends a request for vehicle state information such as the deterioration level variable Rd related to a specified vehicle (S 102 ). When it is determined that the request is received (S 102 : YES), the CPU 122 accesses the storage device 126 to search for the deterioration level variable Rd corresponding to the vehicle ID (S 104 ). Then, the CPU 122 operates the communication device 129 to output, for example, the deterioration level variable Rd corresponding to the requested vehicle ID to the terminal 130 as state information of the used vehicle (S 106 ).
  • step S 106 When step S 106 is completed or when it is determined in step S 102 that the request is not received, the CPU 122 temporarily ends the process illustrated in (b) in FIG. 14 .
  • Steps S 96 to S 98 are specified in the deterioration detection main program 124 a .
  • Steps S 100 to S 106 are specified in the state information provision program 124 b.
  • the deterioration level variable Rd is stored and updated together with the vehicle ID in the center 120 .
  • the used vehicle dealer is provided with the deterioration level variable Rd as state information indicating the state of the used vehicle. Therefore, a customer who considers purchasing the used vehicle is able to obtain detailed information regarding the deterioration level of the vehicle that the customer considers purchasing.
  • the execution device namely, the processing circuitry, corresponds to the CPU 72 and the ROM 74 .
  • the first predetermined period and the second predetermined period correspond to the periods in which the upstream average values Afuave (1) to Afuave (sn) are sampled.
  • the acquisition process corresponds to steps S 10 and S 10 c .
  • the deterioration level variable calculation process corresponds to steps S 12 and S 14 ; steps S 12 a , S 14 a , and S 52 ; steps S 12 b and S 14 b ; and steps S 12 c and S 14 c .
  • the dealing process corresponds to step S 18 and the process illustrated in FIG. 4 .
  • the time series data related to the excess amount variable corresponds to the time series data of each of the upstream average value Afuave, the rotational speed NE, and the charging efficiency q in the process illustrated in FIG. 3 .
  • the downstream detection variable corresponds to the time series data of the downstream average value Afdave.
  • the selection process corresponds to step S 62 and steps S 70 to S 78 .
  • the predetermined condition corresponds to the conditions (A) and (B) in step S 80 .
  • the predetermined condition corresponds to the condition (C) in step S 80 .
  • the predetermined condition corresponds to a condition that is determined in step S 82 whether or not the condition is satisfied.
  • the limiting process corresponds to steps S 34 and S 36 . More specifically, the limiting process corresponds to, for example, a process that operates the inverters 58 and 60 , which are predetermined hardware, to increase the output of the motor-generators 52 and 54 so that the internal combustion engine 10 is set to the stop state.
  • the catalyst deterioration detection system corresponds to the control device 70 and the center 120 .
  • the value corresponding to the oxygen storage amount corresponds to the deterioration level variable Rd.
  • the first execution device corresponds to the CPU 72 and the ROM 74 .
  • the second execution device corresponds to the CPU 122 and the ROM 124 .
  • the acquisition process corresponds to step S 90 .
  • the vehicle side transmission process corresponds to step S 92 .
  • the vehicle side reception process corresponds to step S 94 .
  • the outside reception process corresponds to step S 96 .
  • the oxygen storage amount calculation process corresponds to steps S 12 d and S 14 d .
  • the vehicle side transmission process corresponds to step S 98 .
  • the data analysis device corresponds to the center 120 .
  • the control device of the internal combustion engine corresponds to the control device 70 .
  • the acquisition process corresponds to step S 90 .
  • the deterioration level variable calculation process corresponds to steps S 12 d and S 14 d .
  • the storage process corresponds to step S 100 .
  • the output process corresponds to step S 106 .
  • the computer corresponds to the CPUs 72 and 122 and ROMs 74 and 124 .
  • the first predetermined period which is the sampling period of the time series data of the upstream average value Afuave
  • the second predetermined period which is the sampling period of the time series data of the downstream average value Afdave
  • the first predetermined period and the second predetermined period are not limited to being the same period.
  • the second predetermined period may be a period that is slightly delayed in relation to the first predetermined period. In this case, when the flow rate of the fluid flowing into the upstream catalyst 32 is large, the delay time of the second predetermined period in relation to the first predetermined period may be shortened as compared to when the flow rate of the fluid is small.
  • the delay time may be a fixed value to reduce the number of man-hours for performing the adaptation.
  • the length of the first predetermined period does not necessarily have to be the same as the length of the second predetermined period.
  • the time series data of the excess amount variable is not limited to those provided as examples in the above-described embodiments. More specifically, the time series data of the excess amount variable is not limited to a data set of the three state variables, that is, the upstream average value Afuave, the rotational speed NE, and the charging efficiency ⁇ where the number of samplings is the same, or a data set of time series data of the upstream average value Afuave, the rotational speed NE, and the charging efficiency ⁇ where the number of samplings of the rotational speed NE and the charging efficiency ⁇ is less than that of the upstream average value Afuave.
  • the in-catalyst flow rate CF may be regarded as a variable that determines the flow rate of the fluid flowing into the upstream catalyst 32 .
  • the intake air amount Ga may be used as a variable that determines the flow rate of the fluid flowing into the upstream catalyst 32 and is included in the time series data of the excess amount variable.
  • the time series data of the excess amount variable may be configured by the time series data of the upstream average value Afuave in the predetermined period and a single piece of data of a variable in the predetermined period that determines the flow rate of the fluid flowing into the upstream catalyst 32 .
  • the time series data of the excess amount variable may be the time series data of the upstream average value Afuave in the predetermined period and a single set of the rotational speed NE and the charging efficiency ⁇ in the predetermined period.
  • the rotational speed NE and the charging efficiency ⁇ are variables that determine the flow rate of the fluid flowing into the upstream catalyst 32 in the predetermined period corresponding to the time series data of the upstream average value Afuave. It is assumed that a change in the flow rate in the predetermined period is negligible.
  • the time series data related to the excess amount variable is not limited to the time series data of the upstream average value Afuave.
  • the time series data of the upstream detection value Afu may be used as the time series data related to the excess amount variable.
  • the excess amount variable is not limited to the data related to the upstream detection value Afu.
  • the integrated value of the request injection amount Qd in the time series data sampling interval and the time series data of the intake air amount Ga may be used.
  • the increase amount Qi and the rotational speed NE may be used.
  • the input to the mapping may be the time series data of the downstream detection value Afd instead of the time series data of the downstream average value Afdave.
  • the input to the mapping may be a single sampling value of the in-catalyst flow rate CF instead of the time series data of the in-catalyst flow rate CF.
  • the in-catalyst flow rate CF is calculated from the rotational speed NE and the charging efficiency ⁇ .
  • the in-catalyst flow rate CF is not limited to being calculated therefrom.
  • a pressure sensor and a temperature sensor may be provided upstream of the upstream catalyst 32 in the exhaust passage 30 in the proximity of the upstream catalyst 32 , and the in-catalyst flow rate CF may be calculated based on values from these sensors and the intake air amount Ga.
  • the input to the mapping may be a single sampling value of the catalyst temperature Tcat instead of the time series data of the catalyst temperature Tcat or the time series data of the catalyst temperature average value Tcatave.
  • the catalyst temperature Tcat may be omitted from the input to the mapping.
  • all the inputs of the catalyst temperature calculation process M 22 may be input to the same mapping.
  • the deterioration level variable Rd is also calculated with high accuracy, for example, by increasing the number of the intermediate layers.
  • the set of the rotational speed NE and the charging efficiency ⁇ is used as the operating point variables that specify the operating points of the internal combustion engine 10 .
  • the intake air amount Ga and the rotational speed NE may be used as the operating point variables.
  • the rotational speed NE and the injection amount or the rotational speed NE and the accelerator pedal operating amount ACCP may be used as the operating point variables.
  • the operating point variables do not necessarily have to be included in the input to the mapping.
  • step S 14 d does not necessarily have to be executed in the center 120 .
  • steps S 12 d and S 14 d may be executed in the control device 70 .
  • step S 14 d The input variables provided as examples in step S 14 d may be changed, for example, as follows.
  • the time series data of the increase amount Qi may be used instead of using the time series data of the increase amount average value Qiave.
  • the input variable is not limited to the increase amount Qi or the increase amount average value Qiave.
  • the time series data of an excess rate that is obtained by dividing the increase amount Qi by the base injection amount Qb, the time series data of the average value of the excess rates, or the time series data of the request injection amount Qd or the average value of the request injection amounts Qd may be used.
  • the number of samplings of the time series data that is input to the mapping does not necessarily have to be the same as the number of samplings of the downstream average value Afdave.
  • a single sampling value of the increase amount Qi, the excess rate, or the request injection amount Qd in the predetermined period may be used.
  • a single sampling value of the increase amount average value Qiave, the average value of the excess rates, or the average value of the request injection amounts Qd in the predetermined period may be used.
  • the average value may be an average value over the predetermined period.
  • the time series data of the ignition timing average value aigave may be used instead of using the time series data of the ignition timing aig.
  • the ignition timing average value aigave and the ignition timing aig are not limited to the time series data.
  • a single sampling value of the ignition timing aig or a single sampling value of the ignition timing average value aigave in the predetermined period may be used.
  • the ignition timing average value aigave may be an average value obtained over the predetermined period.
  • the time series data of the EGR rate average value Regrave instead of using the time series data of the EGR rate average value Regrave, the time series data of the EGR rate Regr itself may be used.
  • the EGR rate Regr and the EGR rate average value Regrave are not limited to the time series data.
  • a single sampling value of the EGR rate Regr or a single sampling value of the EGR rate average value Regrave in the predetermined period may be used.
  • the EGR rate average value Regrave may be an average value obtained over the predetermined period.
  • the time series data of the overlap average value ROave may be used instead of using the time series data of the overlap average value ROave.
  • the overlap average value ROave and the overlap RO are not limited to the time series data.
  • a single sampling value of the overlap RO or a single sampling value of the overlap average value ROave in the predetermined period may be used.
  • the overlap average value ROave may be an average value obtained over the predetermined period.
  • a data set of the valve opening timing of the intake valve 18 and the valve opening timing of the exhaust valve 28 may be used as a variable related to the overlap.
  • the time series data of the injection division ratio average value Kpave instead of the time series data of the injection division ratio Kpave, the time series data of the injection division ratio Kp itself may be used.
  • the injection division ratio average value Kpave and the injection division ratio Kp are not limited to the time series data, and a single sampling value of the injection division ratio Kp or a single sampling value of the injection division ratio average value Kpave in the predetermined period may be used.
  • the injection division ratio average value Kpave may be an average value over the predetermined period.
  • the variables related to the injection division ratio Kp do not necessarily have to be included in the input to the mapping in processes such as the process illustrated in (b) in FIG. 14 .
  • a fuel property variable that is, a variable indicating the properties of the fuel, is not limited to a stoichiometric air-fuel ratio variable indicating the difference of the stoichiometric air-fuel ratio of the fuel such as the alcohol concentration Dal.
  • the fuel property variable may be, for example, a variable indicating whether the fuel is a heavy fuel or a light fuel.
  • the fuel property variable does not necessarily have to be included in the input to the mapping in processes such as the process illustrated in (b) in FIG. 14 .
  • the average value of the atmospheric pressure Pa in the predetermined period may be used instead of using the atmospheric pressure Pa.
  • the time series data of the atmospheric pressure Pa or the time series data of the average value of the atmospheric pressure Pa in the predetermined period may be used instead of using the atmospheric pressure Pa.
  • the variables related to the atmospheric pressure Pa do not necessarily have to be included in the input to the mapping in processes such as the process illustrated in (b) in FIG. 14 .
  • the average value of the ambient temperatures TO in the predetermined period may be used instead of using the ambient temperature TO.
  • the time series data of the ambient temperature TO or the time series data of the average value of the ambient temperatures TO in the predetermined period may be used.
  • the variables related to the ambient temperature TO do not necessarily have to be included in the input to the mapping in processes such as the process illustrated in (b) in FIG. 14 .
  • the average value of the sulfur deposition amounts Qs in the predetermined period may be used instead of using the sulfur deposition amount Qs.
  • the time series data of the sulfur deposition amount Qs or the time series data of the average value of the sulfur deposition amounts Qs in the predetermined period may be used.
  • the variables related to the sulfur deposition amount Qs do not necessarily have to be included in the input to the mapping in processes such as the processes illustrated in (b) in FIG. 14 .
  • the specification variables determining the specifications of the upstream catalyst 32 are not limited to the three variables, namely, the maximum value Cmax, the length Lud from upstream to downstream, and the support amount Qpm. For example, only one or two of the three parameters may be used.
  • the specification variables do not necessarily have to be included in the input to the mapping in processes such as the process illustrated in (b) in FIG. 14 .
  • the opening degree of the wastegate valve may be included in the input to the mapping. More specifically, the flow of the fluid to the upstream catalyst 32 changes in accordance with the opening degree of the wastegate valve and thus affects consumption of the stored oxygen. Such a situation is learned, when the opening degree is included in the input to the mapping.
  • the cases in which the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 are not input to the mapping are not limited to those provided as examples in the above-described embodiments.
  • the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 do not have to be input to the mapping.
  • the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 do not have to be input to the mapping.
  • the input to the neural network and the input to the regression equation which are described below in the section titled “Algorithm of Machine Learning,” are not limited to being formed of physical quantities each having a single dimension.
  • different kinds of the physical quantities that are input to the mapping and are directly input to the neural network or the regression equation may be analyzed for their main components, and the main components may be directly input to the neural network or the regression equation.
  • the main components do not necessarily have to be only a portion of the input to the neural network or the regression equation.
  • the entirety of the input may be the main components.
  • the map data 76 a and 126 a include data that specifies a mapping that determines the main components.
  • the illustrations in FIG. 11 indicate that the number of the intermediate layers of the neural network is greater than two.
  • the number of intermediate layers is not limited to being greater than two.
  • the number of intermediate layers of the neural network may be reduced to one or two.
  • Such a configuration is readily implemented, for example, when the processes illustrated in FIGS. 9 to 12 are executed, as compared to when the process illustrated in FIG. 14 is executed.
  • the activation functions h1, h2, . . . , and ha are hyperbolic tangents and the activation function f is a ReLU.
  • each of the activation functions h1, h2, . . . , and h ⁇ may be a ReLU.
  • the activation functions h1, h2, . . . , and h ⁇ may be logistic sigmoid functions.
  • the activation function f may be a logistic sigmoid function.
  • the rotational speed NE and the charging efficiency ⁇ are used as the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 , and different pieces of map data are used for each of the areas divided by the rotational speed NE and the charging efficiency ⁇ .
  • the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 are not limited thereto.
  • the intake air amount Ga or the in-catalyst flow rate CF may be used.
  • different pieces of map data are used for each of the areas divided by the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 and the catalyst temperature Tcat.
  • the present disclosure is not limited to the configuration described above.
  • different pieces of map data may be used for each of areas that are divided by the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 .
  • different pieces of map data may be used for each of areas that are divided by the catalyst temperature Tcat.
  • different pieces of map data may be used for each of areas that are divided by the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 .
  • different pieces of map data may be used for each of areas that are divided by the catalyst temperature Tcat.
  • different pieces of map data may be used for each of areas that are divided by the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 and the catalyst temperature Tcat.
  • the input of the map data corresponding to a case in which different kinds of map data are provided is not limited to those provided as examples in the above-described embodiments.
  • the input to the mapping may include the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 .
  • the input to the mapping may include the catalyst temperature Tcat.
  • the input to the mapping may include the variables related to the flow rate of the fluid flowing to the upstream catalyst 32 and the catalyst temperature Tcat.
  • the input to the mapping may also include, for example, variables that do not directly determine the division of an area such as the increase amount average value Qiave.
  • the condition indicating that the flow rate of the fluid flowing into the upstream catalyst 32 is within a predetermined range is a condition indicating that the logical product of the condition (A) and the condition (B) is true.
  • the present disclosure is not limited to such a configuration.
  • a condition indicating that the intake air amount Ga or the in-catalyst flow rate CF is within a predetermined range may be used.
  • the predetermined condition for the sampling of the variables used in calculating the deterioration level variable Rd is a condition indicating that the logical product of the condition indicating that the flow rate of the fluid flowing into the upstream catalyst 32 is within the predetermined range and the condition (C) is true.
  • the present disclosure is not limited to such a configuration. A condition indicating that only either one of the two conditions is satisfied may be used.
  • the input of the map data in the process illustrated in FIG. 11 and its modification examples is not limited to those provided as examples in the above-described embodiments.
  • the input to the mapping may include the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 .
  • the input to the mapping may include the catalyst temperature Tcat.
  • the input to the mapping includes the variables related to the flow rate of the fluid flowing into the upstream catalyst 32 and the catalyst temperature Tcat.
  • the input to the mapping may also include, for example, variables that do not directly determine the condition that allows the sampling of the variables used to calculate the deterioration level variable Rd, such as the increase amount average value Qiave.
  • the predetermined condition for the sampling of the variables which is used to calculate the deterioration level variable Rd is a condition indicating that the logical sum of the condition (D) and the condition (E) is true.
  • the present disclosure is not limited to such a configuration.
  • a condition indicating that the condition (D) is satisfied may be used.
  • the target value Af* when the target value Af* is set as described above, time series data that is input to the mapping is sampled.
  • the target value Af* may be set for the calculation of the deterioration level variable Rd.
  • the target value Af* when the time series data provided as examples in the above-described embodiments are used, the target value Af* may be set to further reduce the amount of deviation of the composition of the fluid flowing into the catalyst from the composition that is appropriate for the purification performance of the catalyst and to further shorten a period in which the amount of deviation increases, as compared to a case in which the technique in the related art is used.
  • the notification process is not limited to operating a device that outputs visual information such as the warning lamp 99 .
  • the notification process may be, for example, a process that operates a device that outputs voice information.
  • the dealing process is not limited to performing all of steps S 34 , S 36 , and S 38 .
  • steps S 34 , S 36 , and S 38 may be performed Among the three steps, only one step may be performed. Alternatively, for example, only two of the three steps may be performed. Without executing the process illustrated in FIG. 4 , only step S 18 may be executed.
  • the operating amount of the heating control may be changed in accordance with the deterioration level of the upstream catalyst 32 .
  • the operating amount in a case in which the same operating amount is set for when the deterioration level is high and when the deterioration level is low, since the temperature rise speed is lower when the deterioration level is high, the operating amount basically may be changed to increase the temperature rise speed.
  • An algorithm of machine learning is not limited to a neural network.
  • a regression equation may be used. This is equivalent to a case in which intermediate layers are not provided in the above-described neural network.
  • the data acquired when the internal combustion engine 10 operates in a state where the dynamometer 100 is connected to the crankshaft 26 is used as training data.
  • the present disclosure is not limited to such a configuration.
  • data that is acquired when the internal combustion engine 10 is driven in a state where the internal combustion engine 10 is installed in the vehicle VC may be used as training data.
  • the center 120 may execute the process that calculates the maximum value Cmax, and may transmit the maximum value Cmax to the vehicle VC as in steps S 12 a and S 14 a.
  • the center 120 may execute steps S 16 and S 18 , and may execute, as step S 18 , a process that notifies a mobile terminal of a user that there is an abnormality.
  • Steps S 96 , S 12 d , S 14 d , and S 98 illustrated in (b) in FIG. 14 may be executed, for example, by the mobile terminal held by the user.
  • the execution device is not limited to including the CPU 72 (CPU 122 ) and the ROM 74 (ROM 124 ), and executing software processes by using the CPU 72 (CPU 122 ) and the ROM 74 (ROM 124 ).
  • the execution device may include a dedicated hardware circuit (for example, ⁇ IC) that processes at least some of the software processes executed in the above-described embodiments. More specifically, the execution device may have any one of the following configurations (a) to (c).
  • Configuration (a) includes a processing device that executes all of the above-described processes according to a program and a program storage device such as a ROM that stores the program.
  • Configuration (b) includes a processing device that executes some of the above-described processes according to a program, a program storage device, and a dedicated hardware circuit that executes the remaining processes.
  • Configuration (c) includes a dedicated hardware circuit that executes all of the above-described processes.
  • a plurality of software execution devices each of which includes the processing device and the program storage device, may be provided.
  • a plurality of dedicated hardware circuits may be provided. More specifically, the above-described processes may be executed by processing circuitry including at least one of one or more of software execution devices and one or more of dedicated hardware circuits.
  • the program storage device namely, a computer readable medium includes all useable media that can be accessed by general-purpose or dedicated computers.
  • the storage devices that store the map data 76 a and 126 a are configured to be separate from the storage devices (ROM 74 and ROM 124 ) that store the deterioration detection program 74 a and the deterioration detection main program 124 a .
  • the storage devices are not limited to such a configuration.
  • steps S 12 d and S 14 d are performed in the center 120 .
  • the present disclosure is not limited to such a configuration.
  • Steps S 12 d and S 14 d may be performed by the control device 70 .
  • the control device 70 may output the deterioration level variable Rd to the center 120 , together with the vehicle ID, and the center 120 may execute the steps following S 100 .
  • the control device 70 calculates the deterioration level variable Rd
  • the control device 70 does not necessarily have to output the deterioration level variable Rd to the center 120 , together with the vehicle ID.
  • the deterioration level variable Rd may be registered to the center 120 when a used vehicle dealer purchases the vehicle VC as a used vehicle.
  • the internal combustion engine is not limited to including both of the port injection valve 16 and the in-cylinder injection valve 22 .
  • the internal combustion engine may include only one of the two types of the fuel injection valves.
  • the internal combustion engine is not limited to a spark ignition internal combustion engine, and may be, for example, a compression ignition internal combustion engine that uses diesel as fuel.
  • the vehicle is not limited to a series and parallel hybrid vehicle, and may be, for example, a series hybrid vehicle or a parallel hybrid vehicle.
  • the vehicle is also not limited to a hybrid vehicle, and may be a vehicle that includes only the internal combustion engine as a device that generates propulsion power of the vehicle.
  • the catalyst is not limited to a three-way catalyst, and may have, for example, a configuration where a three-way catalyst is supported on a filter that captures particulate matter.

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  • Chemical Kinetics & Catalysis (AREA)
  • Health & Medical Sciences (AREA)
  • Toxicology (AREA)
  • Exhaust Gas After Treatment (AREA)
  • Electrical Control Of Air Or Fuel Supplied To Internal-Combustion Engine (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
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JP6787463B1 (ja) * 2019-09-27 2020-11-18 トヨタ自動車株式会社 内燃機関の失火の有無の判定装置、内燃機関の排気通路に設けられた触媒の劣化度合いの判定装置、内燃機関の排気通路に設けられた触媒の暖機処理における異常の有無の判定装置、内燃機関の排気通路に設けられたフィルタに捕集されたｐｍ堆積量の判定装置、および内燃機関の排気通路に設けられた空燃比センサの異常の有無の判定装置
US12112836B2 (en) * 2021-06-14 2024-10-08 Chevron U.S.A. Inc. Artificial intelligence directed zeolite synthesis
JPWO2023079791A1 (de) * 2021-11-04 2023-05-11
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US20220003142A1 (en) 2022-01-06
US11473477B2 (en) 2022-10-18
JP2020133467A (ja) 2020-08-31
JP6624319B1 (ja) 2019-12-25

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